Hello all, could anyone explain the exact meaning of parameter nproc? Why different values of nproc give so different critical values, i.e. meanL2BB$computeCritval(0.05,nproc=3) [1] 0.9984853 meanL2BB$computeCritval(0.05,nproc=1) [1] 0.4594827 The strucchange-package description gives "integer specifying for which number of processes Brownian motions should be simulated" - do I need nproc-dimensional Brownian bridge? Thank you in advance! Julia -- View this message in context: http://r.789695.n4.nabble.com/nproc-parameter-in-efpFunctional-tp3972419p3972419.html Sent from the R help mailing list archive at Nabble.com.
nproc parameter in efpFunctional
7 messages · bonda, Achim Zeileis
On Wed, 2 Nov 2011, bonda wrote:
Hello all, could anyone explain the exact meaning of parameter nproc? Why different values of nproc give so different critical values, i.e. meanL2BB$computeCritval(0.05,nproc=3) [1] 0.9984853 meanL2BB$computeCritval(0.05,nproc=1) [1] 0.4594827 The strucchange-package description gives "integer specifying for which number of processes Brownian motions should be simulated" - do I need nproc-dimensional Brownian bridge?
Yes, see the 2006 CSDA paper, especially pages 2998/9.
Thank you in advance! Julia -- View this message in context: http://r.789695.n4.nabble.com/nproc-parameter-in-efpFunctional-tp3972419p3972419.html Sent from the R help mailing list archive at Nabble.com.
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Thank you. I've understood, that it should be k (number of parameters) separate Brownian bridges. Is it possible, to get such separated/disaggregated processes also in function efp()? (one can take gefp(..., family=gaussian), or construct by myself residuals(lm.model)*X, but still interesting). And on the contrary, how can I get an aggregated Brownian bridge path for all parameters together, similar to efp()$process? It is made in plot.gefp, but only for graphical visualization... Thank you in advance! Julia -- View this message in context: http://r.789695.n4.nabble.com/nproc-parameter-in-efpFunctional-tp3972419p3984605.html Sent from the R help mailing list archive at Nabble.com.
On Thu, 3 Nov 2011, bonda wrote:
Thank you. I've understood, that it should be k (number of parameters) separate Brownian bridges.
Well, if you use a process based on OLS residuals, you have always a one-dimensional process even though your model has k parameters. Hence, the two parameters are really conceptually different..
Is it possible, to get such separated/disaggregated processes also in function efp()? (one can take gefp(..., family=gaussian), or construct by myself residuals(lm.model)*X, but still interesting).
Some processes that efp() computes are always 1-dimensional (namely those based on residuals) while some are k-dimensional (namely the estimates-based processes) and some are (k+1)-dimensional (the score-based processes). gefp() generalizes this concept and lets you construct the fluctuation processes fairly flexibly.
And on the contrary, how can I get an aggregated Brownian bridge path for all parameters together, similar to efp()$process? It is made in plot.gefp, but only for graphical visualization...
For "gefp" objects all aggregation is done by the efpFunctional employed. But this is really described in a fair amount of detail in the accompanying papers. Specifically, for gefp/efpFunctional in the 2006 CSDA paper.
Thank you in advance! Julia -- View this message in context: http://r.789695.n4.nabble.com/nproc-parameter-in-efpFunctional-tp3972419p3984605.html Sent from the R help mailing list archive at Nabble.com.
______________________________________________ R-help at r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
1 day later
The 2006 CSDA paper is really very informative, perhaps, I'm trying to understand the things lying beyond. If we have e.g. k=3, then taking nproc=3 for the functional maxBB we get a critical value (boundary) maxBB$computeCritval(0.05,nproc=3) [1] 1.544421, and this for nproc=NULL (Bonferroni approximation) will be maxBB$computeCritval(0.05) [1] 1.358099. Aggregating 3 Brownian bridges first over components, we obtain time series process. Now, we wonder if maximum value of the process (aggregation over time) lies over boundary. Which boundary - 1.544421 or 1.358099 - should one take? They look too different and, for instance, lead to "unfair computing" of empirical size (as rejection rate of null hypothesis) or empirical power (as acception rate of alternative). -- View this message in context: http://r.789695.n4.nabble.com/nproc-parameter-in-efpFunctional-tp3972419p3989598.html Sent from the R help mailing list archive at Nabble.com.
On Fri, 4 Nov 2011, bonda wrote:
The 2006 CSDA paper is really very informative, perhaps, I'm trying to understand the things lying beyond. If we have e.g. k=3, then taking nproc=3 for the functional maxBB we get a critical value (boundary) maxBB$computeCritval(0.05,nproc=3) [1] 1.544421, and this for nproc=NULL (Bonferroni approximation) will be maxBB$computeCritval(0.05) [1] 1.358099.
No. In the latter case no Bonferroni approximation is applied. If you want to use it, you can do so via the rule of thumb R> maxBB$computeCritval(0.05/3, nproc = 1) [1] 1.547175 which essentially matches the critical value computed for nproc = 3. If you use the more precise value 1 - (1 - 0.05)^(1/3) instead of 0.05/3, you get a match (up to some small numerical differences). Setting nproc=NULL is only possible in efpFunctional(): efpFunctional() sets up the computeCritval() and computePval() functions via simulation methods (unless closed form solutions are supplied). For the simulation two strategies are available: Simulate nproc = 1, 2, 3, ... explicitly. Simulate only nproc = 1 and apply a Bonferroni correction. The last option is chosen if you set nproc=NULL -- it makes only sense if you aggregate via the maximum across the components. The resulting computeCritval() and computePval() function always need to have the correct nproc supplied (i.e., nproc=NULL makes no sense).
Aggregating 3 Brownian bridges first over components, we obtain time series process. Now, we wonder if maximum value of the process (aggregation over time) lies over boundary. Which boundary - 1.544421 or 1.358099 - should one take? They look too different and, for instance, lead to "unfair computing" of empirical size (as rejection rate of null hypothesis) or empirical power (as acception rate of alternative). -- View this message in context: http://r.789695.n4.nabble.com/nproc-parameter-in-efpFunctional-tp3972419p3989598.html Sent from the R help mailing list archive at Nabble.com.
______________________________________________ R-help at r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
2 days later
Thank you very much, it works now! Best regards, J -- View this message in context: http://r.789695.n4.nabble.com/nproc-parameter-in-efpFunctional-tp3972419p3998747.html Sent from the R help mailing list archive at Nabble.com.